Deep Learning-Based Detection and Classification of Aquatic Animals: Challenges and Opportunities

Vichael A. Babak(Center for Epidemiology and Animal Health, United States Department of Agriculture, Fort Collins, Colorado)
Stiven J. Sweny(Center for Epidemiology and Animal Health, United States Department of Agriculture, Fort Collins, Colorado)

Abstract

Deep learning-based approaches have emerged as promising tools for automating the detection and classification of aquatic animals, offering significant advancements in marine ecology, fisheries management, and environmental monitoring. This paper provides a comprehensive review of the challenges and opportunities associated with implementing deep learning methods in aquatic science. We discuss the significance of automated aquatic animal detection and classification, highlighting the limitations of traditional methods and the potential benefits of deep learning approaches. Key challenges in the application of deep learning to aquatic environments, including data scarcity, class imbalance, and underwater image distortion, are identified and explored. Additionally, we examine emerging opportunities for advancement, such as the integration of underwater robotics, autonomous vehicles, and sensor networks. By addressing these challenges and seizing opportunities for innovation, deep learning holds great promise for revolutionizing aquatic science and enhancing our understanding of marine ecosystems. This review contributes to the ongoing dialogue on the role of deep learning in aquatic research and provides valuable insights for researchers, practitioners, and policymakers seeking to leverage technology for sustainable management of aquatic resources.

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DOI: http://dx.doi.org/10.26549/jfs.v5i2.15907